Are neighbors friends or foes? Assessing the agglomeration effect for Airbnb listings
Tuesday, January 14, 2020
“Location, location, location.”
Location is often perceived as the most important factor when people assess the value of a home or a property. In the lodging industry, location is an essential attribute of a property and can significantly affect a hotel’s financial performance.
Airbnb and the broader home-sharing businesses represent a new form of lodging products. Location is also a significant, influential factor that affects travelers’ purchasing decisions of a home-sharing stay.
Recent research suggests that Airbnb listings are usually found in popular locations such as tourist attractions and points of interest. When more Airbnb listings are located in the same neighborhood, the competition will become more intense.
Intense competition can be harmful to businesses, especially when they enter a price war. If that’s the case, why would Airbnb hosts choose to operate their short-term residential rental businesses in a neighborhood with other, already existing listings? Would it be better if an Airbnb listing is located in a neighborhood with little competition?
The agglomeration theory
Economists argue that proximity in location for businesses providing similar services or products may allow them to profit from the positive externalities in the market. Such positive externalities include increased demands and production enhancements.
Consumers, for example, would like to shop or dine in a neighborhood with many retail stores and restaurants. Travelers may want to stay in an area with an abundance of alternatives. When a place is fully booked, they can easily find a nearby place without starting a new search in a less familiar neighborhood.
In another case, clustering in a location may help companies gain knowledge and resource spillover, as well as easy access to specialized labor and resources. It is not surprising to see that a large number of tech firms choose to put their headquarters in Silicon Valley, and many financial firms are located in Manhattan.
The research study and the research questions
To assess the agglomeration effect of the home-sharing sector, I worked with two other researchers, Drs. Karen Xie and Cindy Yoonjoung Heo, on a research project. We aimed to answer two research questions in our study:
1. Would Airbnb listings benefit from agglomeration?
2. Would such an agglomeration effect vary based on the service provider’s experience? That is, is the agglomeration effect uniform across the hosts managing one or more listings and the hosts with various lengths of tenure?
The data and the analysis
To answer the research questions, we built a dataset with three sources of data. First, we obtained the data through AirDNA on the monthly performance of the entire Airbnb listings in 201 zip codes of New York City from May 2015 to April 2016.
Then, we collected a series of data from the hotels located in the same zip codes from Expedia as hotels and Airbnb listings are also competing against one another. Lastly, we included the neighborhood information from the American Community Surveys by the Census Bureau of the United States.
The dependent variable is an Airbnb listing’s RevPAN (measured in the logarithm of the average revenue per available nights in a month). Our independent variables include Airbnb listing agglomeration (measured in the logarithm of the number of listings agglomerated in a zip code where the focal listing is located), host capacity (number of listings simultaneously managed by a host, including the focal listing), and host tenure (number of months elapsed since the focal listing’s operator become an Airbnb host), plus other variables.
We operated the analyses on a stepwise basis. First, we estimated the baseline model with the primary variables only. Then, we included the groups of other variables into our estimations.
The results of our analysis were published in The Cornell Hospitality Quarterly, including:
1. The level of agglomeration is positively associated with an Airbnb’s RevPAN. For each 10% increase of Airbnb supply in the neighborhood, the RevPAN of a listing would increase by 1.27%.
2. The positive effect of agglomeration will decrease as a host manages more listings.
3. The hosts with longer tenure can further strengthen the positive effect of agglomeration on a listing’s revenue performance.
The above findings add new empirical evidence to two streams of literature, including location research in the lodging industry and the ever-growing research regarding the home-sharing businesses. Practically, our findings are expected to assist the webmasters of home-sharing websites, the entrepreneurs who are running a short-term residential business, and the big hotel chains that have already entered the short-term residential rental market in making business decisions regarding a home-sharing facility’s location.
- Proximity in location should be set as a crucial factor when a home-sharing website displays the alternative options to the travelers according to their searching/browsing history.
- The service providers of home-sharing facilities must pay close attention to such an agglomeration of production enhancements and spillover demands when they choose the right locations for the listings.
- Policymakers are advised to treat multi-unit commercial hosts and single-unit “mom-and-pop” hosts differently, according to the second finding.
Do you operate any home-sharing facilities? How critical is location to your business?
If you are a traveler who often stays in a home-sharing facility, how will a listing’s location affect your purchasing decisions?
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